1. Technical Field
The present invention relates to cardiac perfusion magnetic resonance imaging (MRI), and more particularly, to the automated detection of anatomic landmarks and key-frames from perfusion MRI sequences.
2. Discussion of the Related Art
Myocardial first-pass perfusion cardiovascular MRI has significantly advanced over the last decade and has shown a great deal of value in clinical applications for diagnosis and prognosis in heart diseases. However, clinical routine applications of cardiac perfusion MRI are time-consuming due to the analysis of perfusion data.
Precise information on both morphology and functions of the left ventricle (LV) and the right ventricle (RV) is helpful in cardiology. Anatomic landmarks can be used for anchoring these structures of interest. For example, LV blood pool center indicates the location of the LV. Anchoring RV insertion (e.g., the intersection between the RV outer boundary and the LV epicardium) helps analyze LV functions according to American Heart Association myocardial segmentation models.
In perfusion MRI sequences, identifying a key-frame in a consistent manner provides a reference frame to computationally compensate for cardiac motions caused by respiration, irregular heart rates and imperfect cardiac gating. FIG. 1 shows an example of a cardiac perfusion MRI sequence 100. In FIG. 1, the frame index is annotated at the upper-left corner.
Anatomic landmark detection can be formulated into an object detection framework. Learning based object detection approaches have demonstrated their capabilities to handle large variations of an object by exploring a local region, e.g., a context. Conventional two-dimensional (2D) approaches, however, take into account spatial context only, or spatial and temporal contexts separately.